The Modern Grad Student Paradox

I was sitting in the audience during the discussion of the Hacking Ecology 2.0 Ignite session at ESA this year and Josie Simonis, who was on the panel, said something that really resonated with the grad students in the audience and on Twitter. They said that graduate students face a real paradox: grad students need to learn a lot of modern skills to succeed as scientists, but those who are their teachers (the faculty) don’t have the skills and knowledge to teach them.

What is the purpose of graduate school? It seems like a straightforward question at first, but for those pursuing graduate degrees in the sciences, at least, I think the answer is a lot more complex than it used to be. Because the words “school” and “student” are used, it’s reasonable to suppose the purpose of a graduate education is to learn. For a good chunk of an American PhD program – and for full programs in some other countries – that education doesn’t come in the form of classes, as it does for undergraduate education. Instead, the education is more of an apprenticeship – more like the residency that medical doctors undertake after all their classes are complete.

Let’s pretend that the sole purpose of graduate school in ecology is to create scientists who can fill the shoes of their advisors. [1] This is obviously false, as there are far more PhDs created than R1 faculty jobs that can absorb them. But for the purpose of the post, I want to focus on just the academic path. A newly minted assistant professor today probably spent about 10 years as a graduate student and postdoc. That puts the beginning of their professional training around 2005 (give or take), which is just shortly after the Internet took off as a ubiquitous agent of change. So only the very newest advisors came of (professional) age in what I’m going to call the modern research world. And all the rest – the great majority of tenured and tenure-track faculty – learned how to be scientists during a time when the Internet didn’t exist. (Think about that for a moment…)

I use the Internet as a yardstick, as well as an important driver of research culture. There are a lot of other technologies that have undergone enormous change in the past decade, too. Whatever your particular study system is, it’s likely that there are technological devices, tools, or machines that affect how research in that system has changed over the past decade. And even if your research is bare-bones basic – taxonomy, for example – you have still been affected. The plunging price of computer memory and processing power means that how scientific data is recorded, managed, curated, and accessed has changed.

What all this means is that today’s graduate students need to learn all sorts of things that their advisors can’t teach them. Most advisors haven’t had the time (and in some cases the inclination) to keep up with advances in hardware technology, data standards, software, statistics, and communication. I don’t see this as a shortcoming on the part of the advisors, by the way. Instead I see it as a manifestation of the 12 Hats Problem. But it is a very real conundrum for grad students.

What to do about this paradox? I think the first thing to do is to really assess whether graduate programs are meeting the needs of their students. [2] In this, of course, they need to consider not just those aiming for R1 faculty positions, but also students who will take other types of jobs. My whole time as a grad student – and ever since – I’ve heard a yearning from graduate students for more courses in coding and data management and ecologically relevant statistics. Even if there are teachers for these types of courses (and there often aren’t), there’s always the question of what part of the formal education to drop. My suggestion is to drop or condense requirements that focus on memorization. These days, with the Internet, one can look up a factual piece of information in moments. It simply isn’t worth it for most people to learn how many teeth different mammal skulls hold or to memorize plant families. [3] My emphasis here is on “most people.” There will always be niches of science in which it’s much more useful to have these facts in one’s head than at one’s fingertips. But those niches are quite small – not enough for entire courses. And those who need to memorize this information can do so in the apprentice part of the PhD, rather than the classroom part. I think objections to this come mostly from those who like teaching these sort of (sorry to say it) outdated courses.

One possible solution to the lack of teachers is peer training – that is, grad students (and others) training grad students. The Software Carpentry model is one to consider, in which grad students are trained as teachers and then team teach other students coding skills. Short courses and workshops also fill this gap, but have the downsides of typically being expensive to attend and requiring travel (which disenfranchises some groups of students). Another possibility is to leverage online cross-institution training. Perhaps, for example, there’s a faculty member who is perfect at teaching Needed Skill X. Instead of a class just for students at that teacher’s university, that teacher could open up the class online, allowing participation from students at multiple universities. [4] There exists technology to do this, but I’m relatively unfamiliar with it. For cross-university courses to catch on widely, such technology needs to be rather glitch-free and easy to use. Administrative matters, such as course credits and tuition, need to be addressed too. Perhaps one thing that departments should do is to assign faculty to learn specific skills so they can teach them to students subsequently. As in: “Hey, we’re eliminating your course load this year, but in exchange, we expect you to learn the latest in hierarchical Bayesian Statistics (or R coding or database creation and administration or…) and develop a graduate-level course (or workshop or whatever). You will be teaching it for the next five years, and you will be considered the department expert during that time.”

The apprenticeship part of the PhD still confers many important skills. Being able to read the literature critically, being able to ask a good research question, being able to think logically, being able to write – these are all timeless skills. But for the hard skills, we need a new paradigm – one that doesn’t leave graduate students flailing in a research environment that looks very different from the one their advisors grew up in.

8 comments

Interesting read, Margaret, especially given that I’m very aware that I can’t teach my grad students R (they’re way ahead of me) or hierarchichal Bayesian statistics, etc. for exactly the reasons you suggest.

But a couple of buts.

First, you say “Being able to read the literature critically, being able to ask a good research question, being able to think logically, being able to write – these are all timeless skills. But for the hard skills, we need a new paradigm….” Do you really think that asking good research questions, thinking, and writing are the “easy” skills”? They certainly aren’t for me. R (for example) is a trivial piece of technology (deliberate oversimplication!). Last week it was PRIMER, the week before that it was SAS, next week it will be something else. I don’t think my job as an adviser is to teach my students tools like that; I think my job is considerably more difficult than that.

Second: actually, part of my job is to move grad students away from the idea that if they need to learn something along those lines, they should find a course, or ask somebody older! By the time you earn the PhD, you should be able to identify a learning need, and go out and satisfy it. This may be solo, may be in a group of students, or may even by alongside your adviser!

By the way, I totally agree with you about memorizing plant families (e.g.) – for MOST undergrads. But I don’t want to lose all those “outdated” courses. It’s not the memorizing the families that matters – it’s easy to focus on that, but that isn’t really the point. The point is learning how to approach a diverse group. So yes, one might memorize some families; but what one is really learning is the way one goes about doing that. Which kinds of characters are useful and which not? How can one find out? How does one use a key? How does one organize the families to make sense of them in one’s mind? Once you’ve done that with one group (e.g. plant families), then it’s relatively easy to do the same thing with another (e.g. birds, or beetles). But without having done it, organisms are an intimidating swirling mass of unorganized diversity, and that’s a real handicap for a biologist (grad student or not), I think.

By the way: in terms of advisers not knowing the new things: this has always been true. I bet Darwin complained of it!

> Do you really think that asking good research questions, thinking, and writing are the “easy” skills”?

No, of course not. If I thought they were, I’d advocate for abolishment of the entire grad student structure, as there would be little point in even having a graduate advisor. But I would argue that without “modern” skills, those timeless skills are pretty useless these days. If you can’t analyze or manage your data in a modern context, you’re going to be left behind — you’re not going to do science fast enough (by current standards) to get jobs. I don’t think this is necessarily *good*, by the way. But it is how things are.

Also, for *most* ecology graduate students, it’s the modern skills that are going to get them jobs outside of academia. And therefore, I’d argue, are more important overall in the long run.

About learning diverse groups — I don’t think undergrads should do that at all. And I think grad students (who need to) should learn in the apprentice model — what characters are useful, using a key, etc. Just because I know how to identify Midwest grasses doesn’t mean I’ve learned anything about how to identify mammals or insects or fungi. (Groups I’ve tried with varying success: Midwest prairie plants, tropical birds, prairie insects, African mammals.) So I firmly disagree here. It’s all system-specific. As for a mess of species, I totally disagree. My two-year-old can sort animals into reasonably coherent groups. I think that people naturally “get” that you can organize species, from a very young age. (But of course, we’re always wrong, even as scientists… Naming Nature is a fascinating read: https://www.amazon.com/Naming-Nature-Between-Instinct-Science/dp/0393338711/ )

> In terms of advisers not knowing the new things: this has always been true.

Yes, but. I don’t think it’s ever been such a disadvantage to the student before. Technology wasn’t changing quite so rapidly in Darwin’s time…

“Most advisors haven’t had the time (and in some cases the inclination) to keep up with advances in hardware technology, data standards, software, statistics, and communication.”

That’s why it’s only people roughly my age and younger who use email, which first became a thing back when I was an undergrad…oh, wait.

Ok, snark aside (sorry, couldn’t resist), I think the extent to which the quoted claim is true, and even whether it’s true, varies a *lot* from case to case. As Stephen notes, R for instance was taken up much more rapidly than can be explained by hidebound oldsters dying off and taking their SAS punchcards with them to the grave. Rather, a lot of old dogs learned the new trick of using R. Same for, say, Twitter–there are *plenty* of senior profs who use it, and if I recall the survey data I’ve seen correctly, senior profs and grad students are about equally likely to be on it. As for blogging, I note with interest that a disproportionate number of the most avid bloggers in ecology are senior profs, not grad students or postdocs. 🙂 And when it comes to reading blogs, I can tell you that the readership of Dynamic Ecology isn’t disproportionately skewed towards students. Indeed, if anything, our readership skews *towards* more senior people relative to their abundance. From reader surveys, our avid readers are about 35-40% grad students, 25% postdocs, and 25% faculty equally split between pre- and post-tenure faculty.

I do think there’s a good case to be made that our undergraduate statistics training is starting to get out of step with the sort of statistics published in ecology journals. There’s a recent Ecosphere paper with a bunch of data on this (“peak ANOVA” was back in 2002, apparently…). Which, yeah, presumably reflects most profs teaching the stats that they themselves were taught. Though on the other hand, you have to start somewhere and it’s not clear to me what undergrad biostats courses should start with. Probably some mix of some of the stuff they currently cover and…some new stuff. (how’s that for wishy-washy…)

Re: training in coding focused on efficient handling, manipulation, and analysis of datasets, hard for me to judge whether that’s something that needs to be more widely available than it is. I dunno, I don’t feel like I’m well-placed to judge, since in my own work there really isn’t any need for that. The data I collect fits neatly in one spreadsheet, than you very much. But I’m sure that to people with your programming skills, and to the sort of people who show up to hackathon workshops, it seems like a massive unmet need. Like Paul Krugman says, each of us considers the optimal amount to know about X to be “however much I personally happen to know”. 🙂

Re: the value of memorizing plant families, and the skills that get you jobs outside of academia: um, the basic qualification for a lot of environmental consulting positions is being able to ID the local plants…

Email is actually a good point; young people today don’t use it to communicate much!

> R for instance was taken up much more rapidly than can be explained by hidebound oldsters dying off

Actually, I don’t think so. From my personal observations and experiences, many (most?) oldsters no longer do their own data analyses and management. Instead, they rely on their grad students and postdocs and technicians to do it. (Very small labs like yours might be an exception.) Or, if they do some analyses, they use older techniques to explore the data, but then leave the polished analyses to the younger generation.

But yes, of course, some oldsters do keep up — or try to. There is individual variation. But at the scale of the department, I maintain that there usually aren’t enough skilled teachers to teach the grad students what they need. I’ve seen this in three different major universities, and I hear it all the time from grad students in every forum I participate in. Protip for identifying what your grad students want for courses: ask them.

And yes, some ecology is still of manageable spreadsheet size. But I think that that type of ecology is becoming a smaller proportion of ecology overall.

And IDing local plants != memorizing plant families. The former you learn in the field, apprentice-style. The latter you learn in the classroom.

“Protip for identifying what your grad students want for courses: ask them.”

When we ask our grad students that, the answer is “whatever course teaches me, personally, the specific technical skills and tools I need to complete my own dissertation”. Which to a first approximation is different skills for every student, with the exception of stats.

Ask them together as a group. When I was at UMN, during faculty hiring, grad students would meet as a (big) group with prospective faculty. Many prospective faculty would ask what we grad students wanted to see change in our program. The resulting discussions were very interesting and — I think — good indicators of what the program really did need.

Nice post, and definitely a topic I think a lot of grad students can sympathize with. The problem may even be amplified at smaller institutions where the probability of having a professor that specializes in modern modeling techniques or genomics, etc seems smaller.

At least in my mind though, perhaps it’s more of a trade-off than a paradox.

Pre-tenure faculty recently off the job market will probably have those marketable modern skills that grad students seek (they got the job, didn’t they?), but may lack the mentoring experience or guidance that a more established advisor could offer.

We all know that one advisor can’t provide everything, so it’s up to the grad student to decide whether they want to learn modern skills from a newer faculty member, or benefit from the hard-earned wisdom of the faculty who were around before the internet.

That suggests another solution, doesn’t it? Co-advising by early-career / later-career teams for grad students. I definitely see that as a win-win possibility. (I was co-advised by two senior ecologists as a grad student and now have an early-career advisor for postdoc and see the advantages to both.)